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As a car becomes more connected, a countermeasure against automobile thefthas become a significant task in the real world. To respond to automobiletheft, data mining, biometrics, and additional authentication methods areproposed. Among current countermeasures, data mining method is one of theefficient ways to capture the owner driver's unique characteristics. Toidentify the owner driver from thieves, previous works applied variousalgorithms toward driving data. Such data mining methods utilized supervisedlearning, thus required labeled data set. However, it is unrealistic to gatherand apply the thief's driving pattern. To overcome this problem, we proposedriver identification method with GAN. GAN has merit to build identificationmodel by learning the owner driver's data only. We trained GAN only with ownerdriver's data and used trained discriminator to identify the owner driver. Fromactual driving data, we evaluated our identification model recognizes the ownerdriver well. By ensembling various driver authentication methods with theproposed model, we expect industry can develop automobile theft countermeasuresavailable in the real world.

This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks